Almabetter

Why do students need to form a team? AlmaBetter pro program is a journey towards becoming a Data scientist. Students go through lots of phases of ups & downs that might be difficult to cope with if done individually. We encourage students to form a team of up-to 5 members and do group study to remain motivated throughout the journey. How to form a team? There are 3 basic approaches to form a team with members within your cohort : Joining Chatrooms on T-day [post Orientation session] : - Students must join the first onboarding session scheduled for your cohort addressed by the AlmaBetter team. - After this session, students are encouraged to do networking with peers and form a team of Maximum 5 members. - Once the team is formed, students can continue learning with the group and encourage each other toward program completion. What if you have missed the onboarding session? - If you missed the onboarding session or if you joined the program after the start of the cohort you are following. You can post your Introduction in your cohort’s space in the community to form a team or to join existing teams. Community way : Post an Introductory message in your cohort’s community space mentioning the below points : - Self introduction : Name + Previous Background + City - Team requirement : Mention that you are looking for team members within your cohort to carry out the course - Table no. for reference : Mention the table that you will be waiting in along with the given time, so that...

Today we hear the name ChatGPT from most of the people around us. It has become a personal assistant to many. You must have wondered how The answer is Natural Language Processing (NLP), which is all about teaching machines to understand human language. Just like we communicate with each other, NLP allows computers to communicate with humans using natural language. For example, a virtual assistant like Siri or Alexa tries to understand what humans say and respond appropriately. NLP is used in a variety of applications, such as sentiment analysis, language translation, text classification, and more. Table of Contents • Introduction to NLP • Applications of NLP • Natural Language Processing Pipeline • Advantages and Disadvantages • Future scope and Projects in NLP Introduction to NLP NLP or Natural Language Processing, is a technology that helps computers understand, interpret, and generate human language. It allows computers to communicate with humans using natural language, just like we do with each other. The goal of NLP is to make it easier for people to communicate with computers and for computers to understand and process human language. The field of NLP can be categorized into three main parts: Speech Recognition: The conversion of spoken language into written text. Natural Language Understanding (NLU): The capacity of computers to comprehend human language. Natural Language Generation (NLG): The production of human-like language by a computer. Applications of NLP Some...

Pictured (left to right) are Provost and Vice President for Academic Affairs Bobby Fleischman, College of Engineering Technology faculty member Luke Hedman, President Bill Pink, alumnus and 3DXTech President Matt Howlett, Ferris Vice President for University Advancement and Marketing Kathy Mullins and Michael Staley, dean of the College of Engineering Technology. This photo was taken during Howlett's Wednesday, June 7 visit to show off the Gearbox technology he and his company donated to Ferris. For Class of 1996 alumnus Matt Howlett, Ferris State University has always felt like family, like a home away from home. Now in a position to give back, Howlett couldn’t pass up an opportunity to donate a six-figure high-caliber 3D printer to the College of Engineering Technology, which paves the way for future Ferris alums to graduate a step ahead of the competition from other colleges and universities. Valued at nearly $130,000, 3DXTech’s Gearbox HT2 Printer System brings the faculty, staff and students of the College of Engineering Technology within arm’s reach of cutting-edge technology that will provide the training graduates need after college. “We created this 3D printer to answer the question of how to enable the printing of higher-performance materials. Ferris has always been great for my family. I have my son who went here, and he has a family now,” said Howlett, president of 3DXTech, a leading supplier of carbon fiber reinforced 3D printing filaments. “I understand this ...

In the vast realm of web browsing, two giants have been battling it out for supremacy: Microsoft Edge and Google Chrome. These popular browsers have gained millions of users worldwide, each with its own set of features and quirks. Today, we embark on a delightful journey to compare these two titans, all while keeping it fun and engaging. So, fasten your seatbelts and get ready for the ultimate browser showdown! Microsoft Edge vs Google Chrome Round 1: User Interface Let's talk looks, shall we? When it comes to aesthetics, Microsoft Edge is like the dapper gentleman dressed in a sleek and modern suit. Its minimalist design and neatly organized tabs make navigation a breeze. On the other hand, Google Chrome is more like the cool kid on the block, with its colorful logo and familiar layout. It may not be as polished as Edge, but it has a certain charm that keeps users coming back for more. We still have to cover more points to settle Edge vs Chrome debate. Round 2: Speed and Performance In this round, speed demons take center stage. Google Chrome has long been hailed for its lightning-fast performance, loading webpages in the blink of an eye. It's like having the Flash as your personal browsing companion. However, Microsoft Edge has upped its game in recent years, claiming to be even faster than Chrome. It's like witnessing a thrilling race between Usain Bolt and the Road Runner. Both browsers deliver impressive performance, but the winner here is subjective, depending on you...

Ensemble learning is a technique that is used in Machine Learning that combines the outputs of multiple models to make better predictions. The basic idea behind ensemble learning is to train multiple models to solve the same problem and then to combine their outputs in order to produce a better overall result. This approach can be particularly useful when individual models are prone to overfitting or when they are unable to recognize the complexity of the underlying data. Let us explore the different types of Ensemble Learning Techniques in Machine Learning, their strengths and weaknesses, and how they can be applied in different Machine Learning applications in this article. Ensemble learning technique What is Ensemble Learning? Ensemble learning is a Machine Learning technique that involves combining multiple models to improve the accuracy and robustness of predictions. It works by training several models on the same dataset and combining their outputs to produce a final prediction that is more accurate than the individual models. Ensemble Learning is a powerful tool in Machine Learning and has been used to achieve state-of-the-art performance in various fields, including computer vision, natural language processing, and speech recognition. Types of Ensembles Learning Techniques Types of ensembles technique Bagging • Bagging stands for "bootstrap aggregating" and is one of the most widely used ensemble learning techniques. In bagging, multiple models are trained on diffe...